Optimizing Edge Computing Usability with Random Forest Algorithms

This study convincingly demonstrates the immense potential of Random Forest algorithms to significantly enhance usability in edge computing environments by concentrating on critical parameters such as execution time, energy consumption, and scalability. By leveraging the power of the Random Forest technique, we train models using a multitude of decision trees, which not only improves prediction accuracy but also effectively combats overfitting. This approach directly addresses pressing challenges, including limited resources, scalability issues, and the unpredictability of network conditions. Our results are impressive: we achieved a noteworthy reduction in mean execution time by 0.73 seconds and a standard deviation of 1.15 seconds. These metrics highlight the model’s remarkable resilience and reliability in boosting system responsiveness. With these advancements, Random Forest stands out as a formidable solution for the demanding real-time processing needs inherent in edge computing. Our research presents a compelling strategy for establishing robust and effective edge computing infrastructures by closing crucial gaps that currently exist, particularly the lack of scalable solutions for large, heterogeneous edge platforms, adaptability in fluctuating contexts, and real-world validation. In summary, our work lays a solid foundation for further exploration of adaptive Random Forest applications that can seamlessly adjust to dynamic network conditions, reinforcing the significance of edge computing in latency-sensitive, nextgeneration applications.